{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # This mounts your Google Drive to the Colab VM.\n",
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')\n",
    "\n",
    "# # TODO: Enter the foldername in your Drive where you have saved the unzipped\n",
    "# # assignment folder, e.g. 'cs231n/assignments/assignment1/'\n",
    "# FOLDERNAME = None\n",
    "# assert FOLDERNAME is not None, \"[!] Enter the foldername.\"\n",
    "\n",
    "# # Now that we've mounted your Drive, this ensures that\n",
    "# # the Python interpreter of the Colab VM can load\n",
    "# # python files from within it.\n",
    "# import sys\n",
    "# sys.path.append('/content/drive/My Drive/{}'.format(FOLDERNAME))\n",
    "\n",
    "# # This downloads the CIFAR-10 dataset to your Drive\n",
    "# # if it doesn't already exist.\n",
    "# %cd /content/drive/My\\ Drive/$FOLDERNAME/cs231n/datasets/\n",
    "# !bash get_datasets.sh\n",
    "# %cd /content/drive/My\\ Drive/$FOLDERNAME"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multi-Layer Fully Connected Network\n",
    "In this exercise, you will implement a fully connected network with an arbitrary number of hidden layers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read through the `FullyConnectedNet` class in the file `cs231n/classifiers/fc_net.py`.\n",
    "\n",
    "Implement the network initialization, forward pass, and backward pass. Throughout this assignment, you will be implementing layers in `cs231n/layers.py`. You can re-use your implementations for `affine_forward`, `affine_backward`, `relu_forward`, `relu_backward`, and `softmax_loss` from Assignment 1. For right now, don't worry about implementing dropout or batch/layer normalization yet, as you will add those features later.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=========== You can safely ignore the message below if you are NOT working on ConvolutionalNetworks.ipynb ===========\n",
      "\tYou will need to compile a Cython extension for a portion of this assignment.\n",
      "\tThe instructions to do this will be given in a section of the notebook below.\n"
     ]
    }
   ],
   "source": [
    "# Setup cell.\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"] = (10.0, 8.0)  # Set default size of plots.\n",
    "plt.rcParams[\"image.interpolation\"] = \"nearest\"\n",
    "plt.rcParams[\"image.cmap\"] = \"gray\"\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\"Returns relative error.\"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: (49000, 3, 32, 32)\n",
      "y_train: (49000,)\n",
      "X_val: (1000, 3, 32, 32)\n",
      "y_val: (1000,)\n",
      "X_test: (1000, 3, 32, 32)\n",
      "y_test: (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR-10 data.\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in list(data.items()):\n",
    "    print(f\"{k}: {v.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initial Loss and Gradient Check\n",
    "\n",
    "As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization. This is a good way to see if the initial losses seem reasonable.\n",
    "\n",
    "For gradient checking, you should expect to see errors around 1e-7 or less."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running check with reg =  0\n",
      "Initial loss:  2.300479089768492\n",
      "W1 relative error: 1.0252674471656573e-07\n",
      "W2 relative error: 2.212047930816777e-05\n",
      "W3 relative error: 4.5623278145362223e-07\n",
      "b1 relative error: 4.660094372886962e-09\n",
      "b2 relative error: 2.085654124402131e-09\n",
      "b3 relative error: 1.689724888469736e-10\n",
      "Running check with reg =  3.14\n",
      "Initial loss:  7.052114776533016\n",
      "W1 relative error: 3.904542008453064e-09\n",
      "W2 relative error: 6.86942277940646e-08\n",
      "W3 relative error: 3.483989217647104e-08\n",
      "b1 relative error: 1.475242751587128e-08\n",
      "b2 relative error: 1.4615869332918208e-09\n",
      "b3 relative error: 1.3200479211447775e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for reg in [0, 3.14]:\n",
    "    print(\"Running check with reg = \", reg)\n",
    "    model = FullyConnectedNet(\n",
    "        [H1, H2],\n",
    "        input_dim=D,\n",
    "        num_classes=C,\n",
    "        reg=reg,\n",
    "        weight_scale=5e-2,\n",
    "        dtype=np.float64\n",
    "    )\n",
    "\n",
    "    loss, grads = model.loss(X, y)\n",
    "    print(\"Initial loss: \", loss)\n",
    "\n",
    "    # Most of the errors should be on the order of e-7 or smaller.   \n",
    "    # NOTE: It is fine however to see an error for W2 on the order of e-5\n",
    "    # for the check when reg = 0.0\n",
    "    for name in sorted(grads):\n",
    "        f = lambda _: model.loss(X, y)[0]\n",
    "        grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "        print(f\"{name} relative error: {rel_error(grad_num, grads[name])}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As another sanity check, make sure your network can overfit on a small dataset of 50 images. First, we will try a three-layer network with 100 units in each hidden layer. In the following cell, tweak the **learning rate** and **weight initialization scale** to overfit and achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 269.689233\n",
      "(Epoch 0 / 20) train acc: 0.220000; val_acc: 0.113000\n",
      "(Epoch 1 / 20) train acc: 0.260000; val_acc: 0.121000\n",
      "(Epoch 2 / 20) train acc: 0.520000; val_acc: 0.127000\n",
      "(Epoch 3 / 20) train acc: 0.700000; val_acc: 0.108000\n",
      "(Epoch 4 / 20) train acc: 0.760000; val_acc: 0.134000\n",
      "(Epoch 5 / 20) train acc: 0.880000; val_acc: 0.137000\n",
      "(Iteration 11 / 40) loss: 2.760327\n",
      "(Epoch 6 / 20) train acc: 0.920000; val_acc: 0.142000\n",
      "(Epoch 7 / 20) train acc: 0.960000; val_acc: 0.149000\n",
      "(Epoch 8 / 20) train acc: 0.980000; val_acc: 0.153000\n",
      "(Epoch 9 / 20) train acc: 0.980000; val_acc: 0.153000\n",
      "(Epoch 10 / 20) train acc: 0.980000; val_acc: 0.153000\n",
      "(Iteration 21 / 40) loss: 0.000000\n",
      "(Epoch 11 / 20) train acc: 0.980000; val_acc: 0.153000\n",
      "(Epoch 12 / 20) train acc: 0.980000; val_acc: 0.153000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.152000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.152000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.152000\n",
      "(Iteration 31 / 40) loss: 0.000000\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.152000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.152000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.152000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.152000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.152000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a three-layer Net to overfit 50 training examples by \n",
    "# tweaking just the learning rate and initialization scale.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  \"X_train\": data[\"X_train\"][:num_train],\n",
    "  \"y_train\": data[\"y_train\"][:num_train],\n",
    "  \"X_val\": data[\"X_val\"],\n",
    "  \"y_val\": data[\"y_val\"],\n",
    "}\n",
    "\n",
    "weight_scale = 1e-1   # Experiment with this!\n",
    "learning_rate = 1e-3  # Experiment with this!\n",
    "model = FullyConnectedNet(\n",
    "    [100, 100],\n",
    "    weight_scale=weight_scale,\n",
    "    dtype=np.float64\n",
    ")\n",
    "solver = Solver(\n",
    "    model,\n",
    "    small_data,\n",
    "    print_every=10,\n",
    "    num_epochs=20,\n",
    "    batch_size=25,\n",
    "    update_rule=\"sgd\",\n",
    "    optim_config={\"learning_rate\": learning_rate},\n",
    ")\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history)\n",
    "plt.title(\"Training loss history\")\n",
    "plt.xlabel(\"Iteration\")\n",
    "plt.ylabel(\"Training loss\")\n",
    "plt.grid(linestyle='--', linewidth=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, try to use a five-layer network with 100 units on each layer to overfit on 50 training examples. Again, you will have to adjust the learning rate and weight initialization scale, but you should be able to achieve 100% training accuracy within 20 epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 40) loss: 136.874093\n",
      "(Epoch 0 / 20) train acc: 0.160000; val_acc: 0.110000\n",
      "(Epoch 1 / 20) train acc: 0.160000; val_acc: 0.079000\n",
      "(Epoch 2 / 20) train acc: 0.280000; val_acc: 0.099000\n",
      "(Epoch 3 / 20) train acc: 0.460000; val_acc: 0.113000\n",
      "(Epoch 4 / 20) train acc: 0.660000; val_acc: 0.112000\n",
      "(Epoch 5 / 20) train acc: 0.800000; val_acc: 0.127000\n",
      "(Iteration 11 / 40) loss: 1.100622\n",
      "(Epoch 6 / 20) train acc: 0.920000; val_acc: 0.128000\n",
      "(Epoch 7 / 20) train acc: 0.960000; val_acc: 0.128000\n",
      "(Epoch 8 / 20) train acc: 0.960000; val_acc: 0.129000\n",
      "(Epoch 9 / 20) train acc: 0.980000; val_acc: 0.129000\n",
      "(Epoch 10 / 20) train acc: 1.000000; val_acc: 0.131000\n",
      "(Iteration 21 / 40) loss: 0.004944\n",
      "(Epoch 11 / 20) train acc: 1.000000; val_acc: 0.130000\n",
      "(Epoch 12 / 20) train acc: 1.000000; val_acc: 0.130000\n",
      "(Epoch 13 / 20) train acc: 1.000000; val_acc: 0.130000\n",
      "(Epoch 14 / 20) train acc: 1.000000; val_acc: 0.130000\n",
      "(Epoch 15 / 20) train acc: 1.000000; val_acc: 0.130000\n",
      "(Iteration 31 / 40) loss: 0.000188\n",
      "(Epoch 16 / 20) train acc: 1.000000; val_acc: 0.130000\n",
      "(Epoch 17 / 20) train acc: 1.000000; val_acc: 0.130000\n",
      "(Epoch 18 / 20) train acc: 1.000000; val_acc: 0.130000\n",
      "(Epoch 19 / 20) train acc: 1.000000; val_acc: 0.130000\n",
      "(Epoch 20 / 20) train acc: 1.000000; val_acc: 0.130000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO: Use a five-layer Net to overfit 50 training examples by \n",
    "# tweaking just the learning rate and initialization scale.\n",
    "\n",
    "num_train = 50\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "learning_rate = 1e-3  # Experiment with this!\n",
    "weight_scale = 1e-1   # Experiment with this!\n",
    "model = FullyConnectedNet(\n",
    "    [100, 100, 100, 100],\n",
    "    weight_scale=weight_scale,\n",
    "    dtype=np.float64\n",
    ")\n",
    "solver = Solver(\n",
    "    model,\n",
    "    small_data,\n",
    "    print_every=10,\n",
    "    num_epochs=20,\n",
    "    batch_size=25,\n",
    "    update_rule='sgd',\n",
    "    optim_config={'learning_rate': learning_rate},\n",
    ")\n",
    "solver.train()\n",
    "\n",
    "plt.plot(solver.loss_history)\n",
    "plt.title('Training loss history')\n",
    "plt.xlabel('Iteration')\n",
    "plt.ylabel('Training loss')\n",
    "plt.grid(linestyle='--', linewidth=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 1: \n",
    "Did you notice anything about the comparative difficulty of training the three-layer network vs. training the five-layer network? In particular, based on your experience, which network seemed more sensitive to the initialization scale? Why do you think that is the case?\n",
    "\n",
    "## Answer:\n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Update rules\n",
    "So far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We will implement a few of the most commonly used update rules and compare them to vanilla SGD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SGD+Momentum\n",
    "Stochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochastic gradient descent. See the Momentum Update section at http://cs231n.github.io/neural-networks-3/#sgd for more information.\n",
    "\n",
    "Open the file `cs231n/optim.py` and read the documentation at the top of the file to make sure you understand the API. Implement the SGD+momentum update rule in the function `sgd_momentum` and run the following to check your implementation. You should see errors less than e-8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  8.882347033505819e-09\n",
      "velocity error:  4.269287743278663e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.optim import sgd_momentum\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {\"learning_rate\": 1e-3, \"velocity\": v}\n",
    "next_w, _ = sgd_momentum(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [ 0.1406,      0.20738947,  0.27417895,  0.34096842,  0.40775789],\n",
    "  [ 0.47454737,  0.54133684,  0.60812632,  0.67491579,  0.74170526],\n",
    "  [ 0.80849474,  0.87528421,  0.94207368,  1.00886316,  1.07565263],\n",
    "  [ 1.14244211,  1.20923158,  1.27602105,  1.34281053,  1.4096    ]])\n",
    "expected_velocity = np.asarray([\n",
    "  [ 0.5406,      0.55475789,  0.56891579, 0.58307368,  0.59723158],\n",
    "  [ 0.61138947,  0.62554737,  0.63970526,  0.65386316,  0.66802105],\n",
    "  [ 0.68217895,  0.69633684,  0.71049474,  0.72465263,  0.73881053],\n",
    "  [ 0.75296842,  0.76712632,  0.78128421,  0.79544211,  0.8096    ]])\n",
    "\n",
    "# Should see relative errors around e-8 or less\n",
    "print(\"next_w error: \", rel_error(next_w, expected_next_w))\n",
    "print(\"velocity error: \", rel_error(expected_velocity, config[\"velocity\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have done so, run the following to train a six-layer network with both SGD and SGD+momentum. You should see the SGD+momentum update rule converge faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running with  sgd\n",
      "(Iteration 1 / 200) loss: 2.394736\n",
      "(Epoch 0 / 5) train acc: 0.126000; val_acc: 0.149000\n",
      "(Iteration 11 / 200) loss: 2.234928\n",
      "(Iteration 21 / 200) loss: 2.163600\n",
      "(Iteration 31 / 200) loss: 2.096095\n",
      "(Epoch 1 / 5) train acc: 0.247000; val_acc: 0.253000\n",
      "(Iteration 41 / 200) loss: 2.162310\n",
      "(Iteration 51 / 200) loss: 2.087236\n",
      "(Iteration 61 / 200) loss: 2.040194\n",
      "(Iteration 71 / 200) loss: 1.991600\n",
      "(Epoch 2 / 5) train acc: 0.304000; val_acc: 0.270000\n",
      "(Iteration 81 / 200) loss: 1.979138\n",
      "(Iteration 91 / 200) loss: 2.004795\n",
      "(Iteration 101 / 200) loss: 1.911873\n",
      "(Iteration 111 / 200) loss: 1.865527\n",
      "(Epoch 3 / 5) train acc: 0.305000; val_acc: 0.284000\n",
      "(Iteration 121 / 200) loss: 1.779261\n",
      "(Iteration 131 / 200) loss: 1.912509\n",
      "(Iteration 141 / 200) loss: 1.868499\n",
      "(Iteration 151 / 200) loss: 1.903459\n",
      "(Epoch 4 / 5) train acc: 0.355000; val_acc: 0.302000\n",
      "(Iteration 161 / 200) loss: 1.851525\n",
      "(Iteration 171 / 200) loss: 1.870843\n",
      "(Iteration 181 / 200) loss: 1.800223\n",
      "(Iteration 191 / 200) loss: 1.829537\n",
      "(Epoch 5 / 5) train acc: 0.377000; val_acc: 0.311000\n",
      "Running with  sgd_momentum\n",
      "(Iteration 1 / 200) loss: 2.532703\n",
      "(Epoch 0 / 5) train acc: 0.134000; val_acc: 0.123000\n",
      "(Iteration 11 / 200) loss: 2.154213\n",
      "(Iteration 21 / 200) loss: 1.995253\n",
      "(Iteration 31 / 200) loss: 1.943560\n",
      "(Epoch 1 / 5) train acc: 0.329000; val_acc: 0.302000\n",
      "(Iteration 41 / 200) loss: 1.880381\n",
      "(Iteration 51 / 200) loss: 1.687196\n",
      "(Iteration 61 / 200) loss: 1.526103\n",
      "(Iteration 71 / 200) loss: 1.918955\n",
      "(Epoch 2 / 5) train acc: 0.432000; val_acc: 0.337000\n",
      "(Iteration 81 / 200) loss: 1.836803\n",
      "(Iteration 91 / 200) loss: 1.601124\n",
      "(Iteration 101 / 200) loss: 1.535700\n",
      "(Iteration 111 / 200) loss: 1.372863\n",
      "(Epoch 3 / 5) train acc: 0.454000; val_acc: 0.348000\n",
      "(Iteration 121 / 200) loss: 1.646228\n",
      "(Iteration 131 / 200) loss: 1.598975\n",
      "(Iteration 141 / 200) loss: 1.577018\n",
      "(Iteration 151 / 200) loss: 1.242775\n",
      "(Epoch 4 / 5) train acc: 0.517000; val_acc: 0.352000\n",
      "(Iteration 161 / 200) loss: 1.283915\n",
      "(Iteration 171 / 200) loss: 1.391739\n",
      "(Iteration 181 / 200) loss: 1.310850\n",
      "(Iteration 191 / 200) loss: 1.228808\n",
      "(Epoch 5 / 5) train acc: 0.582000; val_acc: 0.375000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_train = 4000\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "\n",
    "for update_rule in ['sgd', 'sgd_momentum']:\n",
    "    print('Running with ', update_rule)\n",
    "    model = FullyConnectedNet(\n",
    "        [100, 100, 100, 100, 100],\n",
    "        weight_scale=5e-2\n",
    "    )\n",
    "\n",
    "    solver = Solver(\n",
    "        model,\n",
    "        small_data,\n",
    "        num_epochs=5,\n",
    "        batch_size=100,\n",
    "        update_rule=update_rule,\n",
    "        optim_config={'learning_rate': 5e-3},\n",
    "        verbose=True,\n",
    "    )\n",
    "    solvers[update_rule] = solver\n",
    "    solver.train()\n",
    "\n",
    "fig, axes = plt.subplots(3, 1, figsize=(15, 15))\n",
    "\n",
    "axes[0].set_title('Training loss')\n",
    "axes[0].set_xlabel('Iteration')\n",
    "axes[1].set_title('Training accuracy')\n",
    "axes[1].set_xlabel('Epoch')\n",
    "axes[2].set_title('Validation accuracy')\n",
    "axes[2].set_xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in solvers.items():\n",
    "    axes[0].plot(solver.loss_history, label=f\"loss_{update_rule}\")\n",
    "    axes[1].plot(solver.train_acc_history, label=f\"train_acc_{update_rule}\")\n",
    "    axes[2].plot(solver.val_acc_history, label=f\"val_acc_{update_rule}\")\n",
    "    \n",
    "for ax in axes:\n",
    "    ax.legend(loc=\"best\", ncol=4)\n",
    "    ax.grid(linestyle='--', linewidth=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RMSProp and Adam\n",
    "RMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.\n",
    "\n",
    "In the file `cs231n/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check your implementations using the tests below.\n",
    "\n",
    "**NOTE:** Please implement the _complete_ Adam update rule (with the bias correction mechanism), not the first simplified version mentioned in the course notes. \n",
    "\n",
    "[1] Tijmen Tieleman and Geoffrey Hinton. \"Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude.\" COURSERA: Neural Networks for Machine Learning 4 (2012).\n",
    "\n",
    "[2] Diederik Kingma and Jimmy Ba, \"Adam: A Method for Stochastic Optimization\", ICLR 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  9.524687511038133e-08\n",
      "cache error:  2.6477955807156126e-09\n"
     ]
    }
   ],
   "source": [
    "# Test RMSProp implementation\n",
    "from cs231n.optim import rmsprop\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "cache = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'cache': cache}\n",
    "next_w, _ = rmsprop(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247],\n",
    "  [-0.132737,   -0.08078555, -0.02881884,  0.02316247,  0.07515774],\n",
    "  [ 0.12716641,  0.17918792,  0.23122175,  0.28326742,  0.33532447],\n",
    "  [ 0.38739248,  0.43947102,  0.49155973,  0.54365823,  0.59576619]])\n",
    "expected_cache = np.asarray([\n",
    "  [ 0.5976,      0.6126277,   0.6277108,   0.64284931,  0.65804321],\n",
    "  [ 0.67329252,  0.68859723,  0.70395734,  0.71937285,  0.73484377],\n",
    "  [ 0.75037008,  0.7659518,   0.78158892,  0.79728144,  0.81302936],\n",
    "  [ 0.82883269,  0.84469141,  0.86060554,  0.87657507,  0.8926    ]])\n",
    "\n",
    "# You should see relative errors around e-7 or less\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('cache error: ', rel_error(expected_cache, config['cache']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "next_w error:  0.025807742611679764\n",
      "v error:  4.208314038113071e-09\n",
      "m error:  4.214963193114416e-09\n"
     ]
    }
   ],
   "source": [
    "# Test Adam implementation\n",
    "from cs231n.optim import adam\n",
    "\n",
    "N, D = 4, 5\n",
    "w = np.linspace(-0.4, 0.6, num=N*D).reshape(N, D)\n",
    "dw = np.linspace(-0.6, 0.4, num=N*D).reshape(N, D)\n",
    "m = np.linspace(0.6, 0.9, num=N*D).reshape(N, D)\n",
    "v = np.linspace(0.7, 0.5, num=N*D).reshape(N, D)\n",
    "\n",
    "config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5}\n",
    "next_w, _ = adam(w, dw, config=config)\n",
    "\n",
    "expected_next_w = np.asarray([\n",
    "  [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977],\n",
    "  [-0.1380274,  -0.08544591, -0.03286534,  0.01971428,  0.0722929],\n",
    "  [ 0.1248705,   0.17744702,  0.23002243,  0.28259667,  0.33516969],\n",
    "  [ 0.38774145,  0.44031188,  0.49288093,  0.54544852,  0.59801459]])\n",
    "expected_v = np.asarray([\n",
    "  [ 0.69966,     0.68908382,  0.67851319,  0.66794809,  0.65738853,],\n",
    "  [ 0.64683452,  0.63628604,  0.6257431,   0.61520571,  0.60467385,],\n",
    "  [ 0.59414753,  0.58362676,  0.57311152,  0.56260183,  0.55209767,],\n",
    "  [ 0.54159906,  0.53110598,  0.52061845,  0.51013645,  0.49966,   ]])\n",
    "expected_m = np.asarray([\n",
    "  [ 0.48,        0.49947368,  0.51894737,  0.53842105,  0.55789474],\n",
    "  [ 0.57736842,  0.59684211,  0.61631579,  0.63578947,  0.65526316],\n",
    "  [ 0.67473684,  0.69421053,  0.71368421,  0.73315789,  0.75263158],\n",
    "  [ 0.77210526,  0.79157895,  0.81105263,  0.83052632,  0.85      ]])\n",
    "\n",
    "# You should see relative errors around e-7 or less\n",
    "print('next_w error: ', rel_error(expected_next_w, next_w))\n",
    "print('v error: ', rel_error(expected_v, config['v']))\n",
    "print('m error: ', rel_error(expected_m, config['m']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have debugged your RMSProp and Adam implementations, run the following to train a pair of deep networks using these new update rules:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running with  adam\n",
      "(Iteration 1 / 200) loss: 3.476928\n",
      "(Epoch 0 / 5) train acc: 0.121000; val_acc: 0.111000\n",
      "(Iteration 11 / 200) loss: 2.271635\n",
      "(Iteration 21 / 200) loss: 2.202647\n",
      "(Iteration 31 / 200) loss: 1.867746\n",
      "(Epoch 1 / 5) train acc: 0.356000; val_acc: 0.348000\n",
      "(Iteration 41 / 200) loss: 1.929829\n",
      "(Iteration 51 / 200) loss: 1.818561\n",
      "(Iteration 61 / 200) loss: 1.963295\n",
      "(Iteration 71 / 200) loss: 1.476843\n",
      "(Epoch 2 / 5) train acc: 0.390000; val_acc: 0.341000\n",
      "(Iteration 81 / 200) loss: 1.756342\n",
      "(Iteration 91 / 200) loss: 1.425538\n",
      "(Iteration 101 / 200) loss: 1.448520\n",
      "(Iteration 111 / 200) loss: 1.382892\n",
      "(Epoch 3 / 5) train acc: 0.477000; val_acc: 0.376000\n",
      "(Iteration 121 / 200) loss: 1.267791\n",
      "(Iteration 131 / 200) loss: 1.526574\n",
      "(Iteration 141 / 200) loss: 1.427894\n",
      "(Iteration 151 / 200) loss: 1.438794\n",
      "(Epoch 4 / 5) train acc: 0.525000; val_acc: 0.371000\n",
      "(Iteration 161 / 200) loss: 1.467007\n",
      "(Iteration 171 / 200) loss: 1.351929\n",
      "(Iteration 181 / 200) loss: 1.189475\n",
      "(Iteration 191 / 200) loss: 1.243281\n",
      "(Epoch 5 / 5) train acc: 0.562000; val_acc: 0.372000\n",
      "\n",
      "Running with  rmsprop\n",
      "(Iteration 1 / 200) loss: 2.589166\n",
      "(Epoch 0 / 5) train acc: 0.119000; val_acc: 0.146000\n",
      "(Iteration 11 / 200) loss: 2.032921\n",
      "(Iteration 21 / 200) loss: 1.897277\n",
      "(Iteration 31 / 200) loss: 1.770793\n",
      "(Epoch 1 / 5) train acc: 0.381000; val_acc: 0.320000\n",
      "(Iteration 41 / 200) loss: 1.895732\n",
      "(Iteration 51 / 200) loss: 1.681091\n",
      "(Iteration 61 / 200) loss: 1.486923\n",
      "(Iteration 71 / 200) loss: 1.628511\n",
      "(Epoch 2 / 5) train acc: 0.423000; val_acc: 0.341000\n",
      "(Iteration 81 / 200) loss: 1.506182\n",
      "(Iteration 91 / 200) loss: 1.600674\n",
      "(Iteration 101 / 200) loss: 1.478501\n",
      "(Iteration 111 / 200) loss: 1.577709\n",
      "(Epoch 3 / 5) train acc: 0.487000; val_acc: 0.355000\n",
      "(Iteration 121 / 200) loss: 1.495931\n",
      "(Iteration 131 / 200) loss: 1.525799\n",
      "(Iteration 141 / 200) loss: 1.552580\n",
      "(Iteration 151 / 200) loss: 1.654283\n",
      "(Epoch 4 / 5) train acc: 0.524000; val_acc: 0.362000\n",
      "(Iteration 161 / 200) loss: 1.589372\n",
      "(Iteration 171 / 200) loss: 1.413529\n",
      "(Iteration 181 / 200) loss: 1.500597\n",
      "(Iteration 191 / 200) loss: 1.367965\n",
      "(Epoch 5 / 5) train acc: 0.537000; val_acc: 0.375000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learning_rates = {'rmsprop': 1e-4, 'adam': 1e-3}\n",
    "for update_rule in ['adam', 'rmsprop']:\n",
    "    print('Running with ', update_rule)\n",
    "    model = FullyConnectedNet(\n",
    "        [100, 100, 100, 100, 100],\n",
    "        weight_scale=5e-2\n",
    "    )\n",
    "    solver = Solver(\n",
    "        model,\n",
    "        small_data,\n",
    "        num_epochs=5,\n",
    "        batch_size=100,\n",
    "        update_rule=update_rule,\n",
    "        optim_config={'learning_rate': learning_rates[update_rule]},\n",
    "        verbose=True\n",
    "    )\n",
    "    solvers[update_rule] = solver\n",
    "    solver.train()\n",
    "    print()\n",
    "    \n",
    "fig, axes = plt.subplots(3, 1, figsize=(15, 15))\n",
    "\n",
    "axes[0].set_title('Training loss')\n",
    "axes[0].set_xlabel('Iteration')\n",
    "axes[1].set_title('Training accuracy')\n",
    "axes[1].set_xlabel('Epoch')\n",
    "axes[2].set_title('Validation accuracy')\n",
    "axes[2].set_xlabel('Epoch')\n",
    "\n",
    "for update_rule, solver in solvers.items():\n",
    "    axes[0].plot(solver.loss_history, label=f\"{update_rule}\")\n",
    "    axes[1].plot(solver.train_acc_history, label=f\"{update_rule}\")\n",
    "    axes[2].plot(solver.val_acc_history, label=f\"{update_rule}\")\n",
    "    \n",
    "for ax in axes:\n",
    "    ax.legend(loc='best', ncol=4)\n",
    "    ax.grid(linestyle='--', linewidth=0.5)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 2:\n",
    "\n",
    "AdaGrad, like Adam, is a per-parameter optimization method that uses the following update rule:\n",
    "\n",
    "```\n",
    "cache += dw**2\n",
    "w += - learning_rate * dw / (np.sqrt(cache) + eps)\n",
    "```\n",
    "\n",
    "John notices that when he was training a network with AdaGrad that the updates became very small, and that his network was learning slowly. Using your knowledge of the AdaGrad update rule, why do you think the updates would become very small? Would Adam have the same issue?\n",
    "\n",
    "\n",
    "## Answer: \n",
    "[FILL THIS IN]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train a Good Model!\n",
    "Train the best fully connected model that you can on CIFAR-10, storing your best model in the `best_model` variable. We require you to get at least 50% accuracy on the validation set using a fully connected network.\n",
    "\n",
    "If you are careful it should be possible to get accuracies above 55%, but we don't require it for this part and won't assign extra credit for doing so. Later in the assignment we will ask you to train the best convolutional network that you can on CIFAR-10, and we would prefer that you spend your effort working on convolutional networks rather than fully connected networks.\n",
    "\n",
    "**Note:** You might find it useful to complete the `BatchNormalization.ipynb` and `Dropout.ipynb` notebooks before completing this part, since those techniques can help you train powerful models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr 1.000000e-01 ws 1.000000e-01 reg 1.000000e-03 val accuracy: 0.136000\n",
      "lr 1.000000e-02 ws 1.000000e-01 reg 1.000000e-03 val accuracy: 0.123000\n",
      "lr 1.000000e-03 ws 1.000000e-01 reg 1.000000e-03 val accuracy: 0.467000\n",
      "lr 1.000000e-01 ws 5.000000e-02 reg 1.000000e-03 val accuracy: 0.149000\n",
      "lr 1.000000e-02 ws 5.000000e-02 reg 1.000000e-03 val accuracy: 0.119000\n",
      "lr 1.000000e-03 ws 5.000000e-02 reg 1.000000e-03 val accuracy: 0.513000\n",
      "lr 1.000000e-01 ws 1.000000e-02 reg 1.000000e-03 val accuracy: 0.173000\n",
      "lr 1.000000e-02 ws 1.000000e-02 reg 1.000000e-03 val accuracy: 0.119000\n",
      "lr 1.000000e-03 ws 1.000000e-02 reg 1.000000e-03 val accuracy: 0.520000\n",
      "lr 1.000000e-01 ws 1.000000e-01 reg 1.000000e-01 val accuracy: 0.119000\n",
      "lr 1.000000e-02 ws 1.000000e-01 reg 1.000000e-01 val accuracy: 0.294000\n",
      "lr 1.000000e-03 ws 1.000000e-01 reg 1.000000e-01 val accuracy: 0.462000\n",
      "lr 1.000000e-01 ws 5.000000e-02 reg 1.000000e-01 val accuracy: 0.140000\n",
      "lr 1.000000e-02 ws 5.000000e-02 reg 1.000000e-01 val accuracy: 0.242000\n",
      "lr 1.000000e-03 ws 5.000000e-02 reg 1.000000e-01 val accuracy: 0.449000\n",
      "lr 1.000000e-01 ws 1.000000e-02 reg 1.000000e-01 val accuracy: 0.196000\n",
      "lr 1.000000e-02 ws 1.000000e-02 reg 1.000000e-01 val accuracy: 0.113000\n",
      "lr 1.000000e-03 ws 1.000000e-02 reg 1.000000e-01 val accuracy: 0.422000\n",
      "lr 1.000000e-01 ws 1.000000e-01 reg 1.000000e+01 val accuracy: 0.119000\n",
      "lr 1.000000e-02 ws 1.000000e-01 reg 1.000000e+01 val accuracy: 0.119000\n",
      "lr 1.000000e-03 ws 1.000000e-01 reg 1.000000e+01 val accuracy: 0.297000\n",
      "lr 1.000000e-01 ws 5.000000e-02 reg 1.000000e+01 val accuracy: 0.132000\n",
      "lr 1.000000e-02 ws 5.000000e-02 reg 1.000000e+01 val accuracy: 0.119000\n",
      "lr 1.000000e-03 ws 5.000000e-02 reg 1.000000e+01 val accuracy: 0.123000\n",
      "lr 1.000000e-01 ws 1.000000e-02 reg 1.000000e+01 val accuracy: 0.119000\n",
      "lr 1.000000e-02 ws 1.000000e-02 reg 1.000000e+01 val accuracy: 0.112000\n",
      "lr 1.000000e-03 ws 1.000000e-02 reg 1.000000e+01 val accuracy: 0.112000\n",
      "lr 1.000000e-01 ws 1.000000e-01 reg 1.000000e+03 val accuracy: 0.148000\n",
      "lr 1.000000e-02 ws 1.000000e-01 reg 1.000000e+03 val accuracy: 0.119000\n",
      "lr 1.000000e-03 ws 1.000000e-01 reg 1.000000e+03 val accuracy: 0.119000\n",
      "lr 1.000000e-01 ws 5.000000e-02 reg 1.000000e+03 val accuracy: 0.113000\n",
      "lr 1.000000e-02 ws 5.000000e-02 reg 1.000000e+03 val accuracy: 0.119000\n",
      "lr 1.000000e-03 ws 5.000000e-02 reg 1.000000e+03 val accuracy: 0.119000\n",
      "lr 1.000000e-01 ws 1.000000e-02 reg 1.000000e+03 val accuracy: 0.119000\n",
      "lr 1.000000e-02 ws 1.000000e-02 reg 1.000000e+03 val accuracy: 0.127000\n",
      "lr 1.000000e-03 ws 1.000000e-02 reg 1.000000e+03 val accuracy: 0.119000\n",
      "best val accuracy: 0.520000\n"
     ]
    }
   ],
   "source": [
    "best_model = None\n",
    "\n",
    "################################################################################\n",
    "# TODO: Train the best FullyConnectedNet that you can on CIFAR-10. You might   #\n",
    "# find batch/layer normalization and dropout useful. Store your best model in  #\n",
    "# the best_model variable.                                                     #\n",
    "################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "best_val = 0\n",
    "\n",
    "learning_rate = [1e-1, 1e-2, 1e-3]\n",
    "weight_scale = [1e-1,5e-2,1e-2]\n",
    "regularization = [1e-3, 1e-1, 1e1, 1e3]\n",
    "\n",
    "for reg in regularization:\n",
    "    for ws in weight_scale:\n",
    "        for lr in learning_rate:\n",
    "            model = FullyConnectedNet([100, 100], reg=reg, weight_scale=ws)\n",
    "            solver = Solver(\n",
    "                model,\n",
    "                data,\n",
    "                num_epochs=20,\n",
    "                batch_size=100,\n",
    "                update_rule='adam',\n",
    "                optim_config={'learning_rate': lr},\n",
    "                verbose=False\n",
    "            )\n",
    "            solver.train()\n",
    "            val_accuracy = solver.best_val_acc\n",
    "            if val_accuracy > best_val:\n",
    "                best_val = val_accuracy\n",
    "                best_model = model\n",
    "            print('lr %e ws %e reg %e val accuracy: %f' % (lr, ws, reg, val_accuracy))\n",
    "\n",
    "print('best val accuracy: %f' % best_val)\n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Your Model!\n",
    "Run your best model on the validation and test sets. You should achieve at least 50% accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set accuracy:  0.52\n",
      "Test set accuracy:  0.467\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())\n",
    "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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